Novel Small-Molecule Scaffolds as Candidates against the SARS Coronavirus 2 Main Protease: A Fragment-Guided in Silico Approach - MDPI
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
molecules
Article
Novel Small-Molecule Scaffolds as Candidates
against the SARS Coronavirus 2 Main Protease:
A Fragment-Guided in Silico Approach
Teresa L. Augustin † , Roxanna Hajbabaie † , Matthew T. Harper and Taufiq Rahman *
Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, UK;
ta451@cam.ac.uk (T.L.A.); rh731@cam.ac.uk (R.H.); mth29@cam.ac.uk (M.T.H.)
* Correspondence: mtur2@cam.ac.uk
† These authors contributed equally to this work.
Received: 18 October 2020; Accepted: 16 November 2020; Published: 24 November 2020
Abstract: The ongoing pandemic caused by the novel coronavirus has been the greatest global
health crisis since the Spanish flu pandemic of 1918. Thus far, severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) has resulted in over 1 million deaths, and there is no cure or vaccine
to date. The recently solved crystal structure of the SARS-CoV-2 main protease has been a major
focus for drug-discovery efforts. Here, we present a fragment-guided approach using ZINCPharmer,
where 17 active fragments known to bind to the catalytic centre of the SARS-CoV-2 main protease
(SARS-CoV-2 Mpro ) were used as pharmacophore queries to search the ZINC databases of natural
compounds and natural derivatives. This search yielded 134 hits that were then subjected to multiple
rounds of in silico analyses, including blind and focused docking against the 3D structure of the
main protease. We scrutinised the poses, scores, and protein–ligand interactions of 15 hits and
selected 7. The scaffolds of the seven hits were structurally distinct from known inhibitor scaffolds,
thus indicating scaffold novelty. Our work presents several novel scaffolds as potential candidates for
experimental validation against SARS-CoV-2 Mpro .
Keywords: coronavirus; COVID-19; SARS-CoV-2 Mpro ; main protease; fragment-guided; drug
discovery; pharmacophore; molecular docking; natural products; in silico
1. Introduction
Since the sudden outbreak of novel severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)
in Wuhan, China in December 2019 [1,2], the resulting pandemic has seriously challenged global
healthcare systems and caused more than 1 million deaths (as of 30 September 2020) [3]. As cases
have reached a global scale, the disease has become a major disruption to people’s daily lives and the
economy [4]. Coronavirus disease 2019 (COVID-19) can result in pneumonia and respiratory failure
that can be fatal [5], especially in the presence of other comorbidity factors. Despite ongoing efforts by
several academic and industrial groups towards developing vaccines against SARS-CoV-2 [6], we are
yet to have any vaccine approved by the Food and Drug Administration (FDA).
During the life cycle of SARS-CoV-2, two large polyproteins are translated and subsequently
cleaved by two viral proteases, main protease SARS-CoV-2 3CL Mpro (more simply, SARS-CoV-2 Mpro )
and a papain-like protease (PLpro ) [7]. The dependence of the virus on Mpro , and given that there
are no similar human proteases, make this protein a promising drug target [8–10]. SARS-CoV-2 Mpro
consists of three domains. Domains I and II comprise β-barrels forming a chymotrypsin structure and
harbouring catalytic dyad histidine 41 (His41), and cysteine 145 (Cys145) [11]. Domain III consists of
α-helices (Figure 1A) [12]. For its catalytic activity, Mpro needs to dimerise, forming contacts with both
Molecules 2020, 25, 5501; doi:10.3390/molecules25235501 www.mdpi.com/journal/molecules
Molecules 2020, 25, 5501 2 of 14
the N- and C-terminal domains of the other protomer [13]. SARS-CoV-2 Mpro shows 96% sequence
similarity with SARS-CoV-1 Mpro , and the root-mean-square deviation (RMSD) between the two free
Molecules 2020, 25, x 3 of 14
enzyme structures is 0.53 Å for all Cα positions [8]. The strong sequence and structural similarity
suggest that Mpro inhibitors developed for SARS-CoV-1 could also be effective against SARS-CoV-2,
kcal/mol). Therefore, we used AutoDock Vina for all our blind and focused docking, with
although none of these inhibitors is an approved treatment so far.
exhaustiveness set to 24.
Figure
Figure 1.1. Representative
Representativetop-ranked
top-ranked poses
poses of noncovalent
of noncovalent inhibitor
inhibitor X77 from X77multiple
from multiple docking
docking programs
programs (cyan sticks) and original cocrystallised pose of X77 (pink sticks) against
(cyan sticks) and original cocrystallised pose of X77 (pink sticks) against severe acute respiratory severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease
pro (M pro) (Protein Data Bank (PDB) ID:
syndrome coronavirus 2 (SARS-CoV-2) main protease (M ) (Protein Data Bank (PDB) ID: 6W63) with
6W63) with root-mean-square-deviation
root-mean-square-deviation (RMSD) values.(RMSD) values.
Catalytic Catalytic
residues, residues,
histidine 41 histidine 41 (His41),
(His41), and cysteineand
145
cysteine 145 (Cys145) highlighted in gold. (A) Cartoon and surface
(Cys145) highlighted in gold. (A) Cartoon and surface representation of SARS-CoV-2representation of SARS-CoV-2
pro crystal
M pro crystal structure (PDB ID: 6W63) with three domains labelled (Domain I: green; Domain II: blue;
structure (PDB ID: 6W63) with three domains labelled (Domain I: green; Domain II: blue; Domain III:
Domain
purple). III:
Zoompurple). Zoom
in shows in showsVina
AutoDock AutoDock Vina
result; (B) result; (B)
SwissDock SwissDock
result; (C) GOLD result; (C) GOLD
result; result;
(D) AutoDock
(D) AutoDock 4.2 result; (E) 2D
4.2 result; (E) 2D structure of X77. structure of X77.
2.3. Evaluation of ZINCPharmer
Drug-discovery Hits Using
efforts against Mpro have primarily involved finding peptidic
Molecular Docking
SARS-CoV-1
and All
small-molecule
initial 134 inhibitors
compounds targeting the active site [12,14].
from ZINCPharmer Peptidic inhibitors
were subjected to blind contain
dockingchemical
against
warhead groups,
monomeric such as M
SARS-CoV-2 Michael
pro. In theacceptors, to formround,
blind-docking a covalent bond with a(i.e.,
the top-ranked specific
withcysteine
the lowest residue
ΔG)
(Cys145) within the active site [12]. The peptidic inhibitor,
poses of most compounds, except for PubChem compound ID (CID) 2754601 (Supporting called N3, (half-maximal inhibitory
concentrationTable
Information, ) = were
(IC50S1), 16.77 in µM) thewas thesite
active firstoftothebeprotease.
cocrystallised
Out ofwith SARS-CoV-2
the 134
pro
molecules,Monly[14]. 15
Several other
scaffolds peptidicInformation,
(Supporting and nonpeptidic Figurecovalent
S2) hadinhibitors
ΔG valueshave since been
comparable to ordescribed
lower than[15], although
that of X77
none~8.5
(ΔG haskcal/mol),
been approvedwhichaswe a cure for COVID-19
arbitrarily used as to date. On
a cut-off. the other
Those hand, noncovalently
15 molecules interacting
were then subjected to
small molecules may produce fewer off-target side effects and toxicity
possible pose refinement through focused docking (Figure 2). The achieved scores in the blind and[16]; therefore, our work focuses
on these.docking
focused The most potent
of the chosennoncovalent
15 molecules inhibitor
were of verySARS-CoV-1 Mpro so of
similar regardless fargrid-size
is ML188 (antiviral
differences
IC 50 =
(Supporting12.9 µM) [7,16,17], and it has also
Information, Table S1 and Figure 2). been suggested as a broad-spectrum inhibitor of coronavirus
MproOuts [18].
of To
thedate, there is only
15 evaluated one SARS-CoV-2
molecules
pro structure (Protein Data Bank (PDB) ID: 6W63)
in focusedMdocking, the top 7 hits (Figure 3) were selected
with a cocrystallised
(highlighted noncovalent
red in Figure 2A) on inhibitor
the basis(X77)
of their[19].
ΔGInterestingly,
values and the X77nature
is similar in structure with
of interactions to potent
the
noncovalent SARS-CoV-1 M pro inhibitor ML188.
protease, and successful blind docking against the active site of the dimeric M (results not shown). pro
Given
All these the urgency
compounds hadfor developingscores
reproducible effectiveanddrugs
poses against
in each COVID-19, Diamonddocking
of the 3 independent Light Source,
runs.
UK (www.diamond.ac.uk) performed a crystallographic
PubChem CID 16424631 had the lowest mean value of −9.9 kcal/mol, followed by PubChem fragment screening campaign (XChem
CIDs
fragment screen) [20] against SARS-CoV-2 M pro . Of these fragments, 23 were found to be noncovalently
17571683 and 17572009, with scores of −9.4 and −9.2 kcal/mol, respectively. All hits are commercially
bound to(Supporting
the active site of SARS-CoV-2 pro 17 of which bound with high confidence. On the basis
available Information, TableMS2). ,While we tried to ensure that there was diversity in the
of these noncovalently bound fragments,
chosen set of scaffolds, PubChem CIDs 17571683 and 17572009 our study aimed tohave identify
verynovel
similar scaffolds from
scaffolds, whichnatural
led
products pro
to similarand derivatives
AutoDock Vinaagainst
scores.the active
Upon site ofinspection,
visual SARS-CoV-2 none M of. our
In total, 134 natural
top seven products
selected and
scaffolds
theirdeemed
was derivatives were obtained
comparable to any through
of the knowna ZINCPharmer
inhibitors of[21] pharmacophore search and
SARS-CoV-1/SARS-CoV-2 Mprosubjected
listed in
pro
the IUPHAR/BPS Guide to Pharmacology [28] and PostEra’s COVID Moonshotbinding
to multiple rounds of molecular docking against SARS-CoV-2 M to evaluate avidity for page
in silico. Docking scores and ligand–protein interactions
(https://covid.postera.ai/covid/structures) [29]. Overall, were31 compared
unique toscaffolds
those of knownwere noncovalent
compared
(Supporting Information, Table S3). When searching the ChEMBL database [30], only PubChem CID
5281696 was flagged up as a 100% match with sciadopitysin, while the other 6 molecules as queries
produced no hits. Interestingly, sciadopitysin (PubChem CID 5281696) was previously found to
inhibit SARS-CoV-1 Mpro in a fluorescence-resonance energy-transfer (FRET) assay (38.4 ± 0.2 μM)
[31], demonstrating the predictive power of our approach.
Molecules 2020, 25, 5501 3 of 14
inhibitor X77. This in silico fragment-guided approach eventually resulted in the identification of
seven novel chemical scaffolds that could be taken further to in vitro testing against SARS-CoV-2 Mpro .
2. Results
2.1. Identification of Initial Hits through Fragment-Derived Pharmacophore-Based Screening
Virtual screening in ZINCPharmer with pharmacophore queries from the 17 fragments known
to noncovalently bind to the active centre of SARS-CoV-2 Mpro [20] led to the identification of 134
scaffolds belonging to ZINC natural products and their derivative subset. The ZINC library was used
as it is a rich source of diverse and often bioactive chemical scaffolds that are commercially available.
The 134 obtained scaffolds were later subjected to blind and focused docking against the Mpro structure.
2.2. Docking-Protocol Validations
Before docking the 134 hits from the ZINCPharmer-based screening, we sought out to evaluate
the accuracy and reproducibility of our docking protocol by performing a validation or self-docking
exercise with AutoDock Vina [22], AutoDock 4.2 [23,24], SwissDock [25], and GOLD [26]. For this,
known inhibitor X77 was blindly (pseudoblindly for GOLD) docked against Mpro , and poses were
ranked on the basis of the default scoring functions of these algorithms. As shown in Figure 1,
AutoDock Vina, in blind docking mode with exhaustiveness of 24, could almost retrieve the original
pose of X77, unlike AutoDock 4.2, GOLD, or SwissDock. The docked pose of AutoDock Vina was very
closely superimposed onto the original pose within the active site of Mpro with an RMSD of ~0.9 Å,
which was well within the acceptable range (≤2 Å) for successful validation of self-docking [27].
Moreover, docked poses and relevant scores (∆G = −8.52 kcal/mol, n = 5) for X77 were highly
reproducible in AutoDock Vina when carried out in 5 independent runs (standard error: 0.06 kcal/mol).
Therefore, we used AutoDock Vina for all our blind and focused docking, with exhaustiveness set to 24.
2.3. Evaluation of ZINCPharmer Hits Using Molecular Docking
All initial 134 compounds from ZINCPharmer were subjected to blind docking against monomeric
SARS-CoV-2 Mpro . In the blind-docking round, the top-ranked (i.e., with the lowest ∆G) poses of most
compounds, except for PubChem compound ID (CID) 2754601 (Supporting Information, Table S1),
were in the active site of the protease. Out of the 134 molecules, only 15 scaffolds (Supporting
Information, Figure S2) had ∆G values comparable to or lower than that of X77 (∆G ~8.5 kcal/mol),
which we arbitrarily used as a cut-off. Those 15 molecules were then subjected to possible pose
refinement through focused docking (Figure 2). The achieved scores in the blind and focused docking
of the chosen 15 molecules were very similar regardless of grid-size differences (Supporting Information,
Table S1 and Figure 2).
Out of the 15 evaluated molecules in focused docking, the top 7 hits (Figure 3) were selected
(highlighted red in Figure 2A) on the basis of their ∆G values and the nature of interactions with
the protease, and successful blind docking against the active site of the dimeric Mpro (results
not shown). All these compounds had reproducible scores and poses in each of the 3 independent
docking runs. PubChem CID 16424631 had the lowest mean value of −9.9 kcal/mol, followed
by PubChem CIDs 17571683 and 17572009, with scores of −9.4 and −9.2 kcal/mol, respectively.
All hits are commercially available (Supporting Information, Table S2). While we tried to ensure
that there was diversity in the chosen set of scaffolds, PubChem CIDs 17571683 and 17572009
have very similar scaffolds, which led to similar AutoDock Vina scores. Upon visual inspection,
none of our top seven selected scaffolds was deemed comparable to any of the known inhibitors of
SARS-CoV-1/SARS-CoV-2 Mpro listed in the IUPHAR/BPS Guide to Pharmacology [28] and PostEra’s
COVID Moonshot page (https://covid.postera.ai/covid/structures) [29]. Overall, 31 unique scaffolds
were compared (Supporting Information, Table S3). When searching the ChEMBL database [30],
only PubChem CID 5281696 was flagged up as a 100% match with sciadopitysin, while the other
Molecules 2020, 25, 5501 4 of 14
6 molecules as queries produced no hits. Interestingly, sciadopitysin (PubChem CID 5281696) was
previously found to inhibit SARS-CoV-1 Mpro in a fluorescence-resonance energy-transfer (FRET) assay
(38.4 ± 0.2 µM) [31], demonstrating the predictive power of our approach.
Molecules 2020, 25, x 4 of 14
Molecules 2020, 25, x 4 of 14
Figure
Figure 2. Overview
2. Overview of ofAutoDock
AutoDock VinaVina scores
scores obtained
obtainedininfocused
focused docking
dockingandandprotein–ligand
protein–ligand
Figure 2. Overview of AutoDock Vina scores obtained in focused docking and protein–ligand
interactions
interactions of a of a selection
selection of of compounds.
compounds. Compound
Compound labels
labelsare PubChem
are PubChem compound
compound IDs (CIDs)
IDs with with
(CIDs)
interactions of a selection of compounds. Compound labels are PubChem compound IDs (CIDs) with
available names shown on the bar. (A) Analysis of protein–ligand interactions using Protein–Ligand
available names
available namesshown
shown ononthethe
bar.
bar.(A)
(A)Analysis of protein–ligandinteractions
interactions using Protein–Ligand
Interaction Profiler (PLIP) and LigPlot + Analysis of protein–ligand using Protein–Ligand
focusing on similar interactions as X77 original cocrystallised
Interaction Profiler (PLIP) and LigPlot +
Interaction Profiler (PLIP) and LigPlot+focusing on similar
focusing on similarinteractions
interactions as as
X77X77 original
original cocrystallised
cocrystallised
control and interactions with catalytic dyad; (B) AutoDock Vina mean predicted energy of interaction
control and and
control interactions
interactions with catalytic
with catalyticdyad;
dyad;(B)
(B) AutoDock
AutoDock Vina Vinamean
mean predicted
predicted energy
energy of interaction
of interaction
scores from three independent runs; (C) frequency of compounds interacting with residues in PLIP
scores from
scores three
from independent
three independent runs;
runs;(C)
(C)frequency of
frequency of compounds
compounds interacting
interacting with
with
and LigPlot . Catalytic residues, histidine 41 (His41), and cysteine 145 (Cys145) are underlined.
+ residues
residues in PLIP
in PLIP
and LigPlot + . Catalytic
and LigPlot +. Catalytic residues, histidine
residues, histidine 41 (His41),
(His41),andandcysteine
cysteine145145
(Cys145) are underlined.
(Cys145) are underlined.
Figure 3. Two-dimensional structures of top seven selected compounds labelled with PubChem CIDs.
Figure
Figure 3. Two-dimensional
3. Two-dimensional structuresofoftop
structures topseven
seven selected
selectedcompounds
compoundslabelled with
labelled PubChem
with CIDs.CIDs.
PubChem
predicting interactions in 3D and LigPlot+ in 2D. Therefore, the two programmes are not directly
comparable, but together they serve the purpose of providing a more holistic picture on potential
protein–ligand interactions. In both PLIP and LigPlot+, most compounds interacted with at least one
of the two catalytic
Molecules 2020, 25, residues
5501 (His41 and Cys145; Figure 2A) via hydrophobic interactions or hydrogen
5 of 14
bonds. The known inhibitor, X77, also only showed interaction with His41 in PLIP (Figure 5), while
both His41 and Cys145
2.4. Protein–Ligand were shown to interact in LigPlot+ (Supporting Information, Figure S3).
Interactions
Generally, there appeared to be more predicted interactions in LigPlot+, which were primarily
All seven chosen molecules seemed to span multiple subpockets within the active site of
hydrophobic, whereas PLIP seemed to highlight more hydrogen bonds, fewer hydrophobic
SARS-CoV-2 Mpro (Figure 4). The poses were also analysed for interactions with the Mpro in
interactions, and overall
Protein–Ligand fewer
Interaction interactions
Profiler (Figure
(PLIP; Figures 5 and6 6)and
andSupporting Information,
LigPlot+ (Supporting Figure S4).
Information,
Therefore,
Figures it S3
is and
notS4),
surprising that fewer
and are compared compounds
in Figure 2A. PLIP and were shown
LigPlot to interact
+ are based with
on different both catalytic
algorithms,
residues
with inPLIP
PLIP, while most
predicting compounds
interactions in 3D anddidLigPlot +
in the predictions fromthe
in 2D. Therefore, LigPlot + .
two programmes are not
Besides
directlycomparing
comparable,the but predicted
together they interactions with theofcatalytic
serve the purpose providingdyad
a morebetween
holistic the compounds,
picture on
potential protein–ligand interactions. In both PLIP and LigPlot + , most compounds interacted with at
we compared interacting residues with those known for X77 (Figure 2A). None of the compounds
least one of the two catalytic residues (His41 and Cys145; Figure 2A) via hydrophobic interactions
had the same predicted interactions as the X77 control (Figure 5 and Supporting Information, Figure
or hydrogen bonds. The known inhibitor, X77, also only showed interaction with His41 in PLIP
S3), but PubChem CIDs 5074221, 5281696 (sciadopitysin), 7173849, and+ 7052206 showed the greatest
(Figure 5), while both His41 and Cys145 were shown to interact in LigPlot (Supporting Information,
similarity
Figure to S3).
X77 in PLIP and
Generally, thereLigPlot +-based analyses (Figure 2A).
appeared to be more predicted interactions in LigPlot+ , which were
Of the 15 molecules that were
primarily hydrophobic, whereas PLIP seemed chosen fortofocused
highlightdocking, more bonds,
more hydrogen than 76%fewerinhydrophobic
their top-ranked
dockedinteractions,
poses interacted
and overall with
fewerHis41, Glu166,
interactions and6Gln189
(Figure (Figures
and Supporting 2C and 6).
Information, SuchS4).
Figure interactions
Therefore, were
it is not surprising
also observed for X77that in fewer compounds
the original were shown to interact
crystallographically with bothpose
obtained catalytic residues
(Figures 2Cin and
PLIP, 5, and
while most compounds did in the predictions from LigPlot +.
Supporting Information, Figure S3).
FigureFigure
4. (A–G). AutoDock Vina poses (cyan sticks) from focused docking against active site of SARS-
4. (A–G). AutoDock Vina poses (cyan sticks) from focused docking against active site of
CoV-2SARS-CoV-2
Mpro, shown as, white
Mpro shown surface. Catalytic
as white surface. dyad dyad
Catalytic residues highlighted
residues in in
highlighted gold.
gold.Compound
Compound labels
are PubChem
labels areCIDs.
PubChem CIDs.
Molecules 2020, 25, 5501 6 of 14
Molecules 2020, 25, x 6 of 14
Molecules 2020, 25, x 6 of 14
Figure 5. Predicted
Predictedinteractions
interactionsin PLIP of X77 ligand (steel(steel
blue) on SARS-CoV-2 Mpro (PDB ID:(PDB
Mpro 6W63,
Figure
Figure5.5. Predicted interactions inin PLIP
PLIP of X77
of X77 ligand
ligand (steel blue)blue) on SARS-CoV-2
on SARS-CoV-2 Mpro (PDB ID: 6W63, ID:
white).
6W63, (A) Original cocrystallised X77 pose; (B) X77 pose from AutoDock Vina. Hydrogen bonds
white).white). (A) Original
(A) Original cocrystallised
cocrystallised X77 pose;X77(B)
pose;
X77(B) X77from
pose poseAutoDock
from AutoDock Vina. Hydrogen
Vina. Hydrogen bonds
shownshown
bonds as blueas lines,
blue and hydrophobic interactions shown as dotted grey lines. Catalytic
Catalyticresidue
shown as blue lines,lines,
and and hydrophobic
hydrophobic interactions
interactions shown
shown as as dotted
dotted grey
grey lines.
lines. Catalytic residue
residue
histidine 41
histidine 41 (His-41) highlighted
highlighted in gold.
histidine 41(His-41)
(His-41) highlighted in in gold.
gold.
Figure 6. (A–G). Predicted interactions in PLIP of docked poses of top seven selected compounds
Figure6.6. (A–G).
Figure (A–G). Predicted
Predicted interactions in PLIP
PLIP of
of docked
docked poses
poses of
of top
top seven
seven selected
selectedcompounds
compounds
(cyan sticks) with SARS-CoV-2 M pro (PDB ID: 6W63, white sticks). Compound labels are PubChem
(cyansticks)
(cyan sticks)with
with SARS-CoV-2
SARS-CoV-2 Mpro
Mpro (PDB
(PDB ID:ID: 6W63,
6W63, white
white sticks).
sticks). Compound
Compound labels
labels are PubChem
are PubChem CIDs.
CIDs. Hydrogen bonds (blue lines), hydrophobic interactions (dotted grey lines), π–π stacking
CIDs. Hydrogen
Hydrogen bonds
bonds (blue (bluehydrophobic
lines), lines), hydrophobic interactions
interactions (dotted
(dotted grey grey
lines), π–πlines),
stackingπ–π stacking
interactions
interactions (dashed green lines), π–cation interactions (dashed orange lines), and salt bridges (dotted
interactions
(dashed green(dashed green lines),
lines), π–cation π–cation (dashed
interactions interactions (dashed
orange orange
lines), lines),
and salt and salt
bridges bridges
(dotted (dotted
yellow line)
yellow line) are shown. Catalytic dyad, His-41, and Cys-145 highlighted in gold.
yellow
are line)Catalytic
shown. are shown. Catalytic
dyad, His-41,dyad, His-41, and
and Cys-145 Cys-145 highlighted
highlighted in gold. in gold.
Molecules 2020, 25, 5501 7 of 14
Besides comparing the predicted interactions with the catalytic dyad between the compounds,
we compared interacting residues with those known for X77 (Figure 2A). None of the compounds had
the same predicted interactions as the X77 control (Figure 5 and Supporting Information, Figure S3),
but PubChem CIDs 5074221, 5281696 (sciadopitysin), 7173849, and 7052206 showed the greatest
similarity to X77 in PLIP and LigPlot+ -based analyses (Figure 2A).
Of the 15 molecules that were chosen for focused docking, more than 76% in their top-ranked
docked poses interacted with His41, Glu166, and Gln189 (Figures 2C and 6). Such interactions were also
observed for X77 in the original crystallographically obtained pose (Figures 2C and 5, and Supporting
Information, Figure S3).
2.5. Ligand Properties
The predicted properties of the ligands are presented in Table 1. Four of the seven compounds
comply with Lipinski’s rule of five (PubChem CIDs: 17571683, 17572009, 1601667, and 50742221). One of
the compounds (PubChem CID: 50742221) had an alert for having a pan-assay interference compound
(PAINS) feature. Six of the compounds were predicted to contain toxic groups, (PubChem CIDs:
16424631, 17571683, 17572009, 1601667, 50742221, and 7173849), also known as Brenk alerts [32].
Three compounds were predicted to have moderate solubility (PubChem CIDs: 17571683, 17572009,
1601667), while the others were predicted to have poor solubility. Most of the compounds were predicted
to theoretically have good oral pharmacokinetics (high gastrointestinal absorption), except for PubChem
CIDs 5281696 and 7173849.
Table 1. SwissADME results of top seven selected compounds labelled with PubChem CIDs.
PubChem CID
Druglikeness Structural Alerts Pharmacokinetics 4 Water Solubility 5
(also Known as)
Meets Lipinski’s rules, PAINS 2 : 0 alerts; Brenk 3 : 1 High gastrointestinal Poorly soluble;
16424631
except MLOGP 1 > 4.15 alert-coumarin (GI) absorption Ilogp 6 : 3.65
PAINS: 0 alerts; Brenk: 1 Moderately soluble;
17571683 Meets Lipinski’s rules High GI absorption
alert-coumarin iLOGP: 3.83
PAINS: 0 alerts; Brenk: 1 Moderately soluble;
17572009 Meets Lipinski’s rules High GI absorption
alert-coumarin iLOGP: 4.40
PAINS: 0 alerts; Brenk: 2 alerts-2 Moderately soluble;
1601667 Meets Lipinski’s rules High GI absorption
polycyclic aromatic hydrocarbons iLOGP: 3.66
5281696 Meets Lipinski’s rules except Poorly soluble;
PAINS: 0 alerts; Brenk: 0 alerts Low GI absorption
(sciadopitysin) MW > 500 iLOGP: 4.65
PAINS: 1 alert-imine phenol;
Poorly soluble;
50742221 Meets Lipinski’s rules Brenk: 3 alerts-imine, nitro group, High GI absorption
iLOGP: 3.15
oxygen–nitrogen single bond
Meets Lipinski’s rules except PAINS: 0 alerts; Brenk: 2
Moderately soluble;
7173849 MW > 500 and hydrogen alerts-more than 2 esters, Low GI absorption
iLOGP: 2.85
bond acceptors > 10 phthalimide
1Moriguchi octanol–water partition coefficient [33], 2 pan-assay interference compounds [34], 3 Brenk alerts to toxic
groups [32], 4 according to BOILED egg method [35], 5 predicted using LogS (Ali) [36], 6 octanol/water partition
coefficient [37].
3. Discussion
Amid the ongoing pandemic, there are currently no licensed antiviral medications to cure
COVID-19 or an approved vaccine to prevent the infection. Given this, there has been an immense
focus on identifying compounds for testing against the coronavirus’ main protease [38,39]. In silico
drug-discovery tools have been widely used to expedite this process [40,41]. Here, we designed
and implemented a unique fragment-guided pharmacophore-based in silico approach. A dual
combination of fragment-derived pharmacophore-based screening and molecular docking was used.
We presented seven novel scaffolds as potential candidates for prospective experimental validation
against the Mpro (Figure 3). We deliberately considered libraries of purchasable natural products
and their derivatives as an important source of diverse bioactive chemical scaffolds, many of which
Molecules 2020, 25, 5501 8 of 14
were utilised as chemical probes, drugs, or leads for developing modern drugs that notably include
antimicrobials [42,43]. While we have not yet found a use for any method similar to ours in identifying
novel chemotypes to test against SARS-CoV-2 Mpro , one of our top seven hits (sciadopitysin) was also
identified in a recent in silico study [44]. Interestingly, sciadopitysin was previously reported to inhibit
SARS-CoV-1 Mpro in vitro with an IC50 of 38.4 µM [31].
Previous in silico work mainly focused on identifying already approved drugs that can be
repurposed against the Mpro [45–52]. By now, this in silico approach has been widely exhausted,
and there are hundreds of hits reported in the literature. Although we acknowledge that drug
repurposing is a highly sought solution given the urgency of the pandemic, we recognise the merit
of exploring a much larger drug or leadlike chemical space for finding novel hits or leads toward
specifically designing potent antiviral agents against the novel coronavirus’ Mpro .
The identification of diverse scaffolds with inhibitory activity against the Mpro can facilitate the
drug-discovery process through scaffold hopping [53,54]. The greater the number of diverse scaffolds
tested against the Mpro is, the more functional data are available to delineate which key ligand groups
and protein amino acids play an important role in ligand–protein binding, and the inhibition of protein
activity [53,55]. For example, the COVID Moonshot Initiative [29] has invited scientists from around
the world to submit structures for in vitro testing to use structure–activity relationships and identify
the most potent scaffolds. For any drug-discovery venture, several distinct chemical classes are always
needed with which to begin, since there is often attrition in the number of scaffolds as they move from
various stages of preclinical development. We confirmed the scaffold novelty of our set by taking two
approaches: the visual assessment of known SARS-CoV-1/SARS-CoV-2 Mpro inhibitors, and a more
objective automated search of the ChEMBL database and the PostEra COVID Moonshot platform.
Inhibitory fragments used in this work were previously shown to target multiple subpockets within
the active Mpro site [20]. Therefore, their pharmacophore features were valuable in identifying hits.
We used the ZINCPharmer tool, which was successfully used to discover novel inhibitors that are
active in vitro [56–59]. Our integrative approach of combining the predictions of two ligand-protein
interaction predictors (PLIP [60] and LigPlot+ [61]) also allowed for us to assess whether the predicted
binding affinity scores for the compounds were reflecting key inhibitory residues in the active site.
Most tested compounds were predicted to interact with at least one of the catalytic residues in
PLIP and LigPlot+ , and had similar interactions to the chosen X77 control ligand. LigPlot+ assesses
interactions in 2D, while PLIP does so in 3D, including more interactions apart from hydrogen
bonds and hydrophobic interactions (salt bridges, π–π stacking, and π–cation interactions). This may
grant PLIP greater reliability. Yoshino et al. (2020) [62] found that His41, Gly143, and Glu166 were
important residues for peptide inhibitors in molecular-dynamics simulations. Our control molecule,
X77, interacted with all three of these residues, while His41 and Glu166 were the two most common
interacting residues within our compound set (Figure 2C).
Our study highlights a novel set of seven commercially available compounds with favourable
predicted free energy of interactions and likely poses against SARS-CoV-2 Mpro . We propose that these
molecules represent potential candidates for prospective in vitro validation against SARS-CoV-2 Mpro .
While our study is solely based on in silico analyses, multiple examples show that AutoDock Vina
scores are correlated with experimental data [63–66]. If proven to bind to and inhibit the protease,
our scaffolds can be subjected to further optimisation through the iterative use of medicinal chemistry,
and further testing against the Mpro to evaluate their effects on its activity. This could significantly
improve their potency, safety, and stability. We also present an in silico pipeline that can be applied to
screen a much larger drug or leadlike chemical space, and this could lead to the identification of more
novel scaffolds to be tested against SARS-CoV-2 Mpro .Molecules 2020, 25, 5501 9 of 14
4. Materials and Methods
4.1. Virtual Screening with Fragment-Based Pharmacophores
The 3D structures of 17 active fragments (Supporting Information, Figure S1) that were
cocrystallised with SARS-CoV-2 Mpro as noncovalent binders to its active site in Diamond Light
Source Mpro XChem Screen [20] were obtained from the PDB. Each fragment was entered as a query
into ZINCPharmer [21] to screen a subset of the ZINC database comprising natural products and
their derivatives [67] in order to identify molecules with similar pharmacophore features (Figure 7).
The pharmacophore properties of the fragments were largely kept as the original. However, to maximise
the chance of finding more hits, some pharmacophore features deemed nonessential for interacting
pro active site were removed.
with the MMolecules 2020, 25, x 9 of 14
Figure 7. In silico workflow in the present study to identify novel chemical scaffolds from natural
In silicoand
Figure 7. products workflow
derivativesin the
that present
could study to identify
be experimentally novel
tested against chemical
SARS-CoV-2 scaffolds
Mpro. from natural
products and derivatives that could be experimentally tested against SARS-CoV-2 Mpro .
4.2. Protein-Structure Preparation
4.2. Protein-Structure Preparation
The 3D structure of SARS-CoV-2 Mpro with cocrystallised noncovalent inhibitor X77 was
obtained from of
The 3D structure PDB (PDB ID: 6W63)
SARS-CoV-2 pro with
M[19]. Preparations of the protein
cocrystallised for dockinginhibitor
noncovalent were madeX77
in ICM-
was obtained
Pro 3.8 [68], including the removal of ligand and water molecules, and the addition of hydrogens.
from PDB (PDB ID: 6W63) [19]. Preparations of the protein for docking were made in ICM-Pro 3.8 [68],
including 4.3.
theMolecular
removalDocking
of ligand and water molecules, and the addition of hydrogens.
Before performing any docking with the ZINCPharmer-derived hits, validation docking was
4.3. Molecular Docking
performed with known inhibitor X77. The latter was blindly docked [69,70] against the entire SARS-
CoV-2 M structure using AutoDock Vina [22]. The highest-ranked (i.e., with the lowest predicted
pro
Before performing any docking with the ZINCPharmer-derived hits, validation docking was
free energy of interaction, ΔG, kcal/mol) docked pose of X77 was superimposed on its original pose
performedpresent
withinknown inhibitor
the crystal structure, X77.
and theThe
RMSD latter was blindly
was calculated dockedv1.1
in DockRMSD [69,70] against
[71]. The average the entire
SARS-CoV-2 M pro structure using AutoDock Vina [22]. The highest-ranked (i.e., with the lowest
ΔG value for X77 was calculated out of 5 independent docking runs. The exhaustiveness value for
predicted docking was setof
free energy interaction, ∆G, kcal/mol) docked pose of X77 was superimposed on its
to 24.
In addition to AutoDock Vina, three other docking programs were initially tested for the blind
original pose present in the crystal structure, and the RMSD was calculated in DockRMSD v1.1 [71].
docking of the X77 control. These programmes included AutoDock 4.2 (AD 4.2) [23,24], GOLD suite
The average ∆G 5.8.0
version value for X77
(CCDC, was calculated
Cambridge, outthe
UK) [26], and of SwissDock
5 independent docking runs. The
server (www.swissdock.ch) exhaustiveness
[25]. For
value for docking wasblind
AD 4.2-based set docking,
to 24. the Lamarckian genetic algorithm was used with a grid box that had the
same dimensions as those for blind docking with AutoDock Vina. Additionally, pseudoblind docking
was performed in GOLD with a larger grid box of 10 Å (instead of the default 6 Å) set around the
centre of mass of the X77 control in the active site. For SwissDock, the blind mode was opted for [25].Molecules 2020, 25, 5501 10 of 14
In addition to AutoDock Vina, three other docking programs were initially tested for the blind
docking of the X77 control. These programmes included AutoDock 4.2 (AD 4.2) [23,24], GOLD suite
version 5.8.0 (CCDC, Cambridge, UK) [26], and the SwissDock server (www.swissdock.ch) [25].
For AD 4.2-based blind docking, the Lamarckian genetic algorithm was used with a grid box that
had the same dimensions as those for blind docking with AutoDock Vina. Additionally, pseudoblind
docking was performed in GOLD with a larger grid box of 10 Å (instead of the default 6 Å) set
around the centre of mass of the X77 control in the active site. For SwissDock, the blind mode was
opted for [25].
All molecules derived from the ZINCPharmer-based virtual screening were initially subjected to
blind docking against the unliganded monomeric Mpro structure using AutoDock Vina. Molecules for
docking were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov) in 3D file format. Following
this unbiased docking approach, subsequent focused docking with a grid centred on the active site
of the Mpro structure was used for a subset of initial hits, the best poses of which had ∆G values
comparable to or lower than that of X77. As with blind docking, the exhaustiveness value was set to 24;
for each molecule, 3 independent docking runs were performed, and the mean predicted interaction of
binding was calculated. Hits scoring well in focused docking were also blindly docked against the
dimeric Mpro structure to elucidate hits potentially binding to the dimer interface, and only selecting
those with predicted binding to the active site.
4.4. Analysis of Protein–Ligand Interactions
Protein–ligand interactions were analysed in the Protein–Ligand Interaction Profiler (PLIP) [60],
and LigPlot+ 4.5.3 [61]. The resulting interaction profiles were compared with the interactions of
known inhibitor X77, and interaction with the catalytic dyad was particularly considered during the
selection of hits.
4.5. Scaffold Novelty
For the evaluation of scaffold novelty, the 2D structures of the chosen hits were visually
compared against all structures listed on the IUPHAR/BPS Guide to Pharmacology [28] and PostEra’s
COVID Moonshot initiative (https://covid.postera.ai/covid/structures) [29] known to be inhibitors
of SARS-CoV-1/SARS-CoV-2 Mpro . PostEra’s COVID Moonshot initiative platform and the entire
ChEMBL database (www.ebi.ac.uk/chembl) [30] were also searched to check for scaffold novelty.
Where available, bioassay results were checked on PubChem profiles to explore whether a particular
compound had previously been tested against the Mpro .
4.6. Exploring Ligand Properties
The SwissADME [32] server (www.swissadme.ch) was used to predict the pharmacokinetic and
toxicological properties of the compounds.
4.7. Figure Creation
A range of programmes were used to aid in figure creation. These included PyMOL 2.4
(https://pymol.org/2), UCSF Chimera 1.14 [72], R Studio [73], and Microsoft PowerPoint 2016. The 2D
chemical structures of ligands were drawn using MarvinSketch 20.16 (ChemAxon Ltd.) [74].
Supplementary Materials: The following are available online. Figure S1: 2D structures of 17 active fragments
from Mpro XChem Screen; Figure S2: 2D structures of tested compounds; Figure S3: X77 LigPlot+ interactions
with Mpro ; Figure S4: top seven hits of LigPlot+ interactions with Mpro ; Table S1: Blind docking scores for all
134 compounds; Table S2: List of vendors, IDs, and SMILES of 15 compounds from focused docking; Table S3:
list of known SARS-CoV-1/SARS-CoV-2 Mpro inhibitors.
Author Contributions: Conceptualisation and methodology, T.L.A., R.H. and T.R.; in silico analyses, T.L.A.
and R.H.; writing—original-draft preparation, T.L.A. and R.H.; writing—review and editing, T.R. and M.T.H.;Molecules 2020, 25, 5501 11 of 14
supervision, T.R. and M.T.H.; funding acquisition, M.T.H. All authors have read and agreed to the published
version of the manuscript.
Funding: M.T.H was supported by the British Heart Foundation, grant number PG/17/45/33071.
Acknowledgments: We thank the Department of Pharmacology, University of Cambridge, for making this
research possible.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study;
in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish
the results.
References
1. Wu, F.; Zhao, S.; Yu, B.; Chen, Y.-M.; Wang, W.; Song, Z.-G.; Hu, Y.; Tao, Z.-W.; Tian, J.-H.; Pei, Y.-Y.; et al.
A new coronavirus associated with human respiratory disease in China. Nature 2020, 579, 265–269. [CrossRef]
2. Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species Severe acute
respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol.
2020, 5, 536–544. [CrossRef] [PubMed]
3. World Health Organization. Coronavirus Disease (COVID-19) Pandemic. Available online: https://www.
who.int/emergencies/diseases/novel-coronavirus-2019 (accessed on 30 September 2020).
4. McKee, M.; Stuckler, D. If the world fails to protect the economy, COVID-19 will damage health not just now
but also in the future. Nat. Med. 2020, 26, 640–642. [CrossRef]
5. Nutho, B.; Mahalapbutr, P.; Hengphasatporn, K.; Pattaranggoon, N.C.; Simanon, N.; Shigeta, Y.;
Hannongbua, S.; Rungrotmongkol, T. Why Are Lopinavir and Ritonavir Effective against the Newly
Emerged Coronavirus 2019? Atomistic Insights into the Inhibitory Mechanisms. Biochemistry 2020, 59,
1769–1779. [CrossRef] [PubMed]
6. Krammer, F. SARS-CoV-2 vaccines in development. Nat. Cell Biol. 2020, 586, 516–527. [CrossRef] [PubMed]
7. Macchiagodena, M.; Pagliai, M.; Procacci, P. Identification of potential binders of the main protease 3CLpro
of the COVID-19 via structure-based ligand design and molecular modeling. Chem. Phys. Lett. 2020, 750,
137489. [CrossRef]
8. Zhang, L.; Lin, D.; Sun, X.; Curth, U.; Drosten, C.; Sauerhering, L.; Becker, S.; Rox, K.; Hilgenfeld, R. Crystal
structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors.
Science 2020, 368, 409–412. [CrossRef]
9. Yang, H.; Yang, M.; Ding, Y.; Liu, Y.; Lou, Z.; Zhou, Z.; Sun, L.; Mo, L.; Ye, S.; Pang, H.; et al. The crystal
structures of severe acute respiratory syndrome virus main protease and its complex with an inhibitor.
Proc. Natl. Acad. Sci. USA 2003, 100, 13190–13195. [CrossRef]
10. Anand, K. Coronavirus Main Proteinase (3CLpro) Structure: Basis for Design of Anti-SARS Drugs. Science
2003, 300, 1763–1767. [CrossRef]
11. Ionescu, M.I. An Overview of the Crystallized Structures of the SARS-CoV-2. Protein J. 2020, 24, 1–19.
[CrossRef]
12. Pillaiyar, T.; Manickam, M.; Namasivayam, V.; Hayashi, Y.; Jung, S.-H. An Overview of Severe Acute
Respiratory Syndrome–Coronavirus (SARS-CoV) 3CL Protease Inhibitors: Peptidomimetics and Small
Molecule Chemotherapy. J. Med. Chem. 2016, 59, 6595–6628. [CrossRef] [PubMed]
13. Zhong, N.; Zhang, S.; Zou, P.; Chen, J.; Kang, X.; Li, Z.; Liang, C.; Jin, C.; Xia, B. Without Its N-Finger,
the Main Protease of Severe Acute Respiratory Syndrome Coronavirus Can Form a Novel Dimer through Its
C-Terminal Domain. J. Virol. 2008, 82, 4227–4234. [CrossRef] [PubMed]
14. Jin, Z.; Du, X.; Xu, Y.; Deng, Y.; Liu, M.; Zhao, Y.; Zhang, B.; Li, X.; Zhang, L.; Peng, C.; et al. Structure of
M(pro) from SARS-CoV-2 and discovery of its inhibitors. Nature 2020, 582, 289–293. [CrossRef]
15. Reddy, V.P.; Elkhateeb, E.; Jo, H.; Natalie, N.; Lythgoe, E.; Tang, W.; Jamei, M.; Sharma, S.; Hodjegan, A.R.
Pharmacokinetics under the COVID-19 storm! Drug Targets Potential Treat. J. Med. Chem. 2020. [CrossRef]
16. Jacobs, J.; Grum-Tokars, V.; Zhou, Y.; Turlington, M.; Saldanha, S.A.; Chase, P.; Eggler, A.; Dawson, E.S.;
Baez-Santos, Y.M.; Tomar, S.; et al. Discovery, Synthesis, and Structure-Based Optimization of a Series of
N-(tert-Butyl)-2-(N-arylamido)-2-(pyridin-3-yl) Acetamides (ML188) as Potent Noncovalent Small Molecule
Inhibitors of the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) 3CL Protease. J. Med. Chem.
2013, 56, 534–546. [CrossRef] [PubMed]Molecules 2020, 25, 5501 12 of 14
17. Ul Qamar, M.T.; Alqahtani, S.M.; Alamri, M.A.; Chen, L.L. Structural basis of SARS-CoV-2 3CL(pro) and
anti-COVID-19 drug discovery from medicinal plants. J. Pharm. Anal. 2020, 10, 313–319. [CrossRef]
18. Berry, M.; Fielding, B.; Gamieldien, J. Human coronavirus OC43 3CL protease and the potential of ML188 as
a broad-spectrum lead compound: Homology modelling and molecular dynamic studies. BMC Struct. Biol.
2015, 15, 1–10. [CrossRef]
19. Mesecar, A.D. A taxonomically-driven approach to development of potent, broad-spectrum inhibitors of
coronavirus main protease including SARS-CoV-2 (COVID-19). 2020. Unpublished work.
20. Douangamath, A.; Fearon, D.; Gehrtz, P.; Krojer, T.; Lukacik, P.; Owen, C.D.; Resnick, E.; Strain-Damerell, C.;
Aimon, A.; Ábrányi-Balogh, P.; et al. Crystallographic and electrophilic fragment screening of the SARS-CoV-2
main protease. Nat. Commun. 2020, 11, 1–11. [CrossRef]
21. Koes, D.R.; Camacho, C.J. ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Res.
2012, 40, W409–W414. [CrossRef]
22. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring
function, efficient optimization, and multithreading. J. Comput. Chem. 2009, 31, 455–461. [CrossRef] [PubMed]
23. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4
and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009, 30,
2785–2791. [CrossRef] [PubMed]
24. Huey, R.; Morris, G.M.; Olson, A.J.; Goodsell, D.S. A semiempirical free energy force field with charge-based
desolvation. J. Comput. Chem. 2007, 28, 1145–1152. [CrossRef] [PubMed]
25. Grosdidier, A.; Zoete, V.; Michielin, O. Fast docking using the CHARMM force field with EADock DSS.
J. Comput. Chem. 2011, 32, 2149–2159. [CrossRef]
26. Jones, G.H.; Willett, P.; Glen, R.C.; Leach, A.R.; Taylor, R. Development and validation of a genetic algorithm
for flexible docking 1 Edited by F. E. Cohen. J. Mol. Biol. 1997, 267, 727–748. [CrossRef]
27. Ding, Y.; Fang, Y.; Moreno, J.; Ramanujam, J.; Jarrell, M.; Brylinski, M. Assessing the similarity of ligand
binding conformations with the Contact Mode Score. Comput. Biol. Chem. 2016, 64, 403–413. [CrossRef]
28. Alexander, S.P.; Ball, J.K.; Tsoleridis, T. Coronavirus (CoV) proteins (version 2020.5) in the IUPHAR/BPS
Guide to Pharmacology Database. IUPHAR/BPS Guide Pharmacol. CITE 2020, 2020. [CrossRef]
29. PostEra COVID Moonshot. Available online: https://covid.postera.ai/covid (accessed on 13 September 2020).
30. Mendez, D.; Gaulton, A.; Bento, A.P.; Chambers, J.; De Veij, M.; Félix, E.; Magariños, M.P.; Mosquera, J.F.;
Mutowo, P.; Nowotka, M.; et al. ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Res.
2019, 47, D930–D940. [CrossRef]
31. Ryu, Y.B.; Jeong, H.J.; Kim, J.H.; Kim, Y.M.; Park, J.Y.; Kim, D.; Nguyen, T.T.; Park, S.J.; Chang, J.S.; Park, K.H.;
et al. Biflavonoids from Torreya nucifera displaying SARS-CoV 3CL(pro) inhibition. Bioorg. Med. Chem. 2010,
18, 7940–7947. [CrossRef]
32. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness
and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717. [CrossRef]
33. Moriguchi, I.; Hirono, S.; Liu, Q.; Nakagome, I.; Matsushita, Y. Simple Method of Calculating Octanol/Water
Partition Coefficient. Chem. Pharm. Bull. 1992, 40, 127–130. [CrossRef]
34. Baell, J.B.; Nissink, J.W.M. Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017—Utility and
Limitations. ACS Chem. Biol. 2018, 13, 36–44. [CrossRef] [PubMed]
35. Daina, A.; Zoete, V. A BOILED-Egg to Predict Gastrointestinal Absorption and Brain Penetration of Small
Molecules. ChemMedChem 2016, 11, 1117–1121. [CrossRef] [PubMed]
36. Ali, J.; Camilleri, P.; Brown, M.B.; Hutt, A.J.; Kirton, S.B. Revisiting the General Solubility Equation:
In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area.
J. Chem. Inf. Model. 2012, 52, 420–428. [CrossRef]
37. Daina, A.; Michielin, O.; Zoete, V. iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water
Partition Coefficient for Drug Design Using the GB/SA Approach. J. Chem. Inf. Model. 2014, 54, 3284–3301.
[CrossRef] [PubMed]
38. Chodera, J.D.; Lee, A.A.; London, N.; Von Delft, F. Crowdsourcing drug discovery for pandemics. Nat. Chem.
2020, 12, 581. [CrossRef]
39. Li, Q.; Kang, C. Progress in Developing Inhibitors of SARS-CoV-2 3C-Like Protease. Microorganisms 2020, 8,
1250. [CrossRef]Molecules 2020, 25, 5501 13 of 14
40. Zhou, Y.; Hou, Y.; Shen, J.; Huang, Y.; Martin, W.; Cheng, F. Network-based drug repurposing for novel
coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov. 2020, 6, 14. [CrossRef]
41. Xu, C.; Ke, Z.; Liu, C.; Wang, Z.; Liu, D.; Zhang, L.; Wang, J.; He, W.; Xu, Z.; Li, Y.; et al. Systemic in Silico
Screening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server.
J. Chem. Inf. Model. 2020, 28. [CrossRef]
42. Carlson, E.E. Natural Products as Chemical Probes. ACS Chem. Biol. 2010, 5, 639–653. [CrossRef]
43. Newman, D.J.; Cragg, G.M. Natural Products as Sources of New Drugs over the Nearly Four Decades from
01/1981 to 09/2019. J. Nat. Prod. 2020, 83, 770–803. [CrossRef]
44. Rana, S.; Sharma, S.; Ghosh, K. Virtual Screening of Naturally Occuring Antiviral Molecules for
SARS-CoV-2 Mitigation Using Docking Tool on Multiple Molecular Targets. ChemRxiv 2020. Available
online: https://chemrxiv.org/articles/preprint/Virtual_Screening_of_Naturally_Occurring_Antiviral_Molecules_
for_SARS-CoV-2_Mitigation_Using_Docking_Tool_on_Multiple_Molecular_Targets/12403940/1 (accessed on
20 November 2020).
45. Riva, L.; Yuan, S.; Yin, X.; Martin-Sancho, L.; Matsunaga, N.; Pache, L.; Burgstaller-Muehlbacher, S.;
De Jesus, P.D.; Teriete, P.; Hull, M.V.; et al. Discovery of SARS-CoV-2 antiviral drugs through large-scale
compound repurposing. Nat. Cell Biol. 2020, 586, 113–119. [CrossRef]
46. Sharma, P.; Vijayan, V.; Pant, P.; Sharma, M.; Vikram, N.; Kaur, P.; Singh, T.P.; Sharma, S. Identification
of potential drug candidates to combat COVID-19: A structural study using the main protease (mpro) of
SARS-CoV-2. J. Biomol. Struct. Dyn. 2020, 1–11. [CrossRef]
47. Kandeel, M.; Al-Nazawi, M. Virtual screening and repurposing of FDA approved drugs against COVID-19
main protease. Life Sci. 2020, 251, 117627. [CrossRef] [PubMed]
48. Kumar, Y.; Singh, H.; Patel, C.N. In silico prediction of potential inhibitors for the main protease of SARS-CoV-2
using molecular docking and dynamics simulation based drug-repurposing. J. Infect. Public Health 2020, 13,
1210–1223. [CrossRef]
49. Lokhande, K.B.; Doiphode, S.; Vyas, R.; Swamy, K.V. Molecular docking and simulation studies on
SARS-CoV-2 Mpro reveals Mitoxantrone, Leucovorin, Birinapant, and Dynasore as potent drugs against
COVID-19. J. Biomol. Struct. Dyn. 2020, 1–12. [CrossRef]
50. Al-Khafaji, K.; Al-Duhaidahawi, D.L.; Tok, T.T. Using integrated computational approaches to identify safe
and rapid treatment for SARS-CoV-2. J. Biomol. Struct. Dyn. 2020, 1–9. [CrossRef]
51. Mahanta, S.; Chowdhury, P.; Gogoi, N.; Goswami, N.; Borah, D.; Kumar, R.; Chetia, D.; Borah, P.;
Buragohain, A.K.; Gogoi, B. Potential anti-viral activity of approved repurposed drug against main protease
of SARS-CoV-2: An in silico based approach. J. Biomol. Struct. Dyn. 2020, 1–10. [CrossRef]
52. Shamsi, A.; Mohammad, T.; Anwar, S.; Alajmi, M.F.; Hussain, A.; Rehman, T.; Islam, A.; Hassan, I. Glecaprevir
and Maraviroc are high-affinity inhibitors of SARS-CoV-2 main protease: Possible implication in COVID-19
therapy. Biosci. Rep. 2020, 40, 6. [CrossRef]
53. Hu, Y.; Stumpfe, D.; Bajorath, J. Recent Advances in Scaffold Hopping. J. Med. Chem. 2016, 60, 1238–1246.
[CrossRef] [PubMed]
54. Grisoni, F.; Merk, D.; Byrne, R.; Schneider, G. Scaffold-Hopping from Synthetic Drugs by Holistic Molecular
Representation. Sci. Rep. 2018, 8, 16469. [CrossRef] [PubMed]
55. Gimeno, A.; Mestres-Truyol, J.; Ojeda-Montes, M.J.; Macip, G.; Saldivar-Espinoza, B.; Cereto-Massagué, A.;
Pujadas, G.; Garcia-Vallvé, S. Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2
through Consensus Docking and Drug Reposition. Int. J. Mol. Sci. 2020, 21, 3793. [CrossRef]
56. Koes, D.R.; Pabon, N.A.; Deng, X.; Phillips, M.A.; Camacho, C.J. A Teach-Discover-Treat Application of
ZincPharmer: An Online Interactive Pharmacophore Modeling and Virtual Screening Tool. PLoS ONE 2015,
10, e0134697. [CrossRef] [PubMed]
57. El Kerdawy, A.M.; Osman, A.A.; Zaater, M. Receptor-based pharmacophore modeling, virtual screening,
and molecular docking studies for the discovery of novel GSK-3β inhibitors. J. Mol. Model. 2019, 25, 171.
[CrossRef] [PubMed]
58. Agrawal, R.; Jain, P.; Dikshit, S.N. Ligand-based pharmacophore detection, screening of potential gliptins
and docking studies to get effective antidiabetic agents. Comb. Chem. High Throughput Screen. 2012, 15,
849–876. [CrossRef] [PubMed]Molecules 2020, 25, 5501 14 of 14
59. Katarkar, A.; Haldar, P.K.; Chaudhuri, K. De novo design based pharmacophore query generation and
virtual screening for the discovery of Hsp-47 inhibitors. Biochem. Biophys. Res. Commun. 2015, 456, 707–713.
[CrossRef]
60. Salentin, S.; Schreiber, S.; Haupt, V.J.; Adasme, M.F.; Schroeder, M. PLIP: Fully automated protein-ligand
interaction profiler. Nucleic Acids Res. 2015, 43, W443–W447. [CrossRef]
61. Laskowski, R.A.; Swindells, M.B. LigPlot+: Multiple Ligand–Protein Interaction Diagrams for Drug Discovery.
J. Chem. Inf. Model. 2011, 51, 2778–2786. [CrossRef]
62. Yoshino, R.; Yasuo, N.; Sekijima, M. Identification of key interactions between SARS-CoV-2 main protease
and inhibitor drug candidates. Sci. Rep. 2020, 10, 1–8. [CrossRef]
63. Chen, P.; Ke, Y.; Lu, Y.; Du, Y.; Li, J.; Yan, H.; Zhao, H.; Zhou, Y.; Yang, Y. DLIGAND2: An improved
knowledge-based energy function for protein–ligand interactions using the distance-scaled, finite, ideal-gas
reference state. J. Cheminform. 2019, 11, 52. [CrossRef] [PubMed]
64. Theerawatanasirikul, S.; Kuo, C.J.; Phetcharat, N.; Lekcharoensuk, P. In silico and in vitro analysis of small
molecules and natural compounds targeting the 3CL protease of feline infectious peritonitis virus. Antivir. Res.
2020, 174, 104697. [CrossRef] [PubMed]
65. Jeong, J.; Kim, H.; Choi, J. In Silico Molecular Docking and In Vivo Validation with Caenorhabditis
elegans to Discover Molecular Initiating Events in Adverse Outcome Pathway Framework: Case Study on
Endocrine-Disrupting Chemicals with Estrogen and Androgen Receptors. Int. J. Mol. Sci. 2019, 20, 1209.
[CrossRef] [PubMed]
66. Cuccioloni, M.; Bonfili, L.; Cecarini, V.; Cocchioni, F.; Petrelli, D.; Crotti, E.; Zanchi, R.; Eleuteri, A.M.;
Angeletti, M. Structure/activity virtual screening and in vitro testing of small molecule inhibitors of
8-hydroxy-5-deazaflavin:NADPH oxidoreductase from gut methanogenic bacteria. Sci. Rep. 2020, 10, 13150.
[CrossRef]
67. Irwin, J.J.; Shoichet, B.K. ZINC-a free database of commercially available compounds for virtual screening.
J. Chem. Inf. Model. 2005, 45, 177–182. [CrossRef]
68. Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM A new method for protein modeling and design: Applications to
docking and structure prediction from the distorted native conformation. J. Comput. Chem. 1994, 15, 488–506.
[CrossRef]
69. Callejo, G.; Pattison, L.A.; Greenhalgh, J.C.; Chakrabarti, S.; Andreopoulou, E.; Hockley, J.R.; Smith, E.S.J.;
Rahman, T. In silico screening of GMQ-like compounds reveals guanabenz and sephin1 as new allosteric
modulators of acid-sensing ion channel 3. Biochem. Pharmacol. 2020, 174, 113834. [CrossRef]
70. Greenhalgh, J.C.; Chandran, A.; Harper, M.T.; Ladds, G.; Rahman, T. Proposed model of the Dictyostelium
cAMP receptors bound to cAMP. J. Mol. Graph. Model. 2020, 100, 107662. [CrossRef]
71. Bell, E.W.; Zhang, Y. DockRMSD: An open-source tool for atom mapping and RMSD calculation of symmetric
molecules through graph isomorphism. J. Cheminform. 2019, 11, 40. [CrossRef]
72. Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E.
UCSF Chimera-A visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25,
1605–1612. [CrossRef]
73. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing:
Vienna, Austria, 2016.
74. ChemAxon. Marvin, 20.16; ChemAxon Ltd.: Budapest, Hungary, 2020.
Sample Availability: Samples of the compounds are not available from the authors.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional
affiliations.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).You can also read
